Generative Adversarial Networks (GAN) are a strong candidate for such fast simulations. Based on two neural networks, generator and discriminator, trained alternatively, GANs have been widely explored thanks to their ability to generate images with complex structures at much high speed. In HEP, the variations of GAN, such as CaloGAN [1]} and 3DGAN [2]}, have achieved similar performance as full Monte Carlo based simulation, but with reduced time taken.
